/**
 * VercelAgentRunner — AgentRunner impl backed by the Vercel AI SDK.
 *
 * Architecture: unlike Claude/Kimi/Codex which spawn subprocess binaries or
 * run the whole agent loop in-process via vendor SDKs, Vercel SDK is a thin
 * HTTP client over each vendor's native API. We compose the agent loop
 * manually via streamText + stopWhen: isStepCount(N), per Vercel v7 docs.
 *
 * Critical design decisions (see plan review §1-4):
 *   1. Session resume: Vercel is stateless; we persist messages to disk via
 *      VercelSessionStore and reload on resume.
 *   2. Tool policy: every tool's execute() wraps args.canUseTool — bypassing
 *      it would defeat the per-role policy engine (denyTools, file scope, etc.)
 *   3. Cost: Vercel returns token usage but no USD; the result carries no
 *      cost_usd (unknown, reported as null — never 0). Token budgets still
 *      enforce via policy.ts.
 *   4. Mailbox consumption: Vercel's streamText takes a single prompt, not an
 *      async iterable. We loop over args.prompt, running one streamText per
 *      mailbox message and accumulating into messages[].
 *
 * Streaming: Vercel streams per-token text-delta events. The runner joins
 * them into one `assistant` message per model step (one org chat event,
 * #563) and yields the raw deltas only when extras.includePartialMessages
 * is set, like the other runners.
 */
import type { AgentMessage, AgentRunArgs, AgentRunner } from './agent-runner.js';
export interface VercelRunnerArgs extends AgentRunArgs {
    /** Vendor slug from the provider config (e.g. 'glm', 'openai', 'deepseek'). */
    vendor?: string;
    /** Full provider config from the role definition. */
    providerConfig?: {
        kind?: string;
        vendor?: string;
        apiKeyEnv?: string;
        baseUrl?: string;
    };
}
export declare class VercelAgentRunner implements AgentRunner {
    run(args: VercelRunnerArgs): AsyncIterable<AgentMessage>;
}
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